System and method for a global digital elevation model

Abstract

A system and method for creating a digital elevation model, and for reducing vertical bias and/or root mean square error (RMSE) of an elevation dataset may be provided. The system may include one or more processors configured to receive input data, provide the input data to a neural network (NN), and generate a digital elevation model based on the predicted elevations output by the NN. The NN may be configured to include an input layer; a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation; and an output layer connected to the plurality of hidden layers, the output layer configured to output a predicted elevation based on the analysis of the input data.

What is claimed is: 
     
         1 . A system for creating a digital elevation model, comprising:
 one or more processors configured to:
 receive input data; 
 provide the input data to a neural network (NN), the NN comprising:
 an input layer; 
 a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation; and 
 an output layer connected to the plurality of hidden layers, the output layer configured to output a predicted elevation based on the analysis of the input data; and 
 
 generate a digital elevation model based on the predicted elevation. 
   
     
     
         2 . The system according to  claim 1 , wherein input data includes vegetation, architecture, and population density information for a plurality of locations. 
     
     
         3 . The system according to  claim 1 , wherein the plurality of hidden layers comprises at least a thousand hidden units. 
     
     
         4 . The system according to  claim 1 , wherein the input layer comprises at least 10 units corresponding to at least 2,000 values of the input data. 
     
     
         5 . The system according to  claim 1 , wherein the output layer comprises one unit. 
     
     
         6 . The system according to  claim 1 , wherein the NN is trained using data from a NASA ICESat-2 mission as ground truth. 
     
     
         7 . The system according to  claim 1 , wherein the NN is configured to predict error corrections for pixels on land between a minimum and maximum elevation. 
     
     
         8 . The system according to  claim 6 , wherein the minimum elevation is −10 m, and the maximum elevation is 120 m. 
     
     
         9 . The system according to  claim 1 , wherein the input data comprises one or more datasets stored on a database operably coupled to at least one of the one or more processors. 
     
     
         10 . The system according to  claim 1 , wherein the one or more processors is further configured to output a graphical map based on the digital elevation model. 
     
     
         11 . The system according to  claim 9 , wherein the one or more processors is further configured to receive user input, and based on the user input, generate the graphical map, where the graphical map shows predicted flood locations, vertical bias of the digital elevation model, or root mean square error (RMSE) of the digital elevation model. 
     
     
         12 . The system according to  claim 10 , further comprising a plurality of remote devices, each remote device configured to display a graphical map generated based on user input sent from the remote device. 
     
     
         13 . The system according to  claim 1 , wherein the NN is a convolution neural network (CNN). 
     
     
         14 . The system according to  claim 1 , wherein the one or more processors are further configured to:
 compare each data element of the digital elevation model to a water height or elevation; and   for each data element,
 assess whether a location represented by the data element is at or below an elevation expected to flood or be inundated based on the water height or elevation; 
 assess whether ground on which an installed infrastructure or environment or planned infrastructure or environment at a location represented by the data element is at or below an elevation expected to flood or be inundated based on the water height or elevation; and/or 
 calculate a depth of a flood at a location represented by the data element based on the water height or elevation whether such water height or elevation is the result of a measurement, prediction, or flood model. 
   
     
     
         15 . A method for creating a digital elevation model, comprising:
 providing input data to a neural network (NN), the NN comprising:
 an input layer; 
 a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation; and 
 an output layer connected to the plurality of hidden layers, the output layer configured to output a predicted elevation based on the analysis of the input data; and 
   generating a digital elevation model based on the predicted elevation for one or more geographic locations.   
     
     
         16 . The method according to  claim 15 , further comprising generating a graphical map based on the digital elevation model. 
     
     
         17 . The method according to  claim 15 , wherein input data includes vegetation, architecture, and population density information for a plurality of locations. 
     
     
         18 . The method according to  claim 15 , wherein the plurality of hidden layers comprises at least a thousand hidden units. 
     
     
         19 . The method according to  claim 15 , wherein the input layer comprises at least 10 units corresponding to at least 2,000 values of the input data. 
     
     
         20 . The method according to  claim 15 , wherein the output layer comprises one unit. 
     
     
         21 . The method according to  claim 15 , wherein the NN is trained using data from a NASA ICESat-2 mission as ground truth. 
     
     
         22 . The method according to  claim 15 , wherein the NN is configured to predict error corrections for pixels on land between a minimum and maximum elevation. 
     
     
         23 . The method according to  claim 22 , wherein the minimum elevation is −10 m, and the maximum elevation is 120 m. 
     
     
         24 . The method according to  claim 15 , wherein the input data comprises one or more datasets stored on a database operably coupled to at least one of the one or more processors. 
     
     
         25 . The method according to  claim 15 , further comprising outputting a graphical map based on the digital elevation model. 
     
     
         26 . The method according to  claim 25 , further comprising receiving user input, and based on the user input, generating the graphical map, where the graphical map shows predicted flood locations, predicted flood depth at each predicted flood location, vertical bias of the digital elevation model, or root mean square error (RMSE) of the digital elevation model. 
     
     
         27 . The method according to  claim 15 , wherein the NN is a convolution neural network (CNN). 
     
     
         28 . The method according to  claim 15 , further comprising:
 comparing each data element of the digital elevation model to a water height or elevation; and   for each data element,
 assessing whether a location represented by the data element is expected to flood or be inundated based on the water height or elevation; 
 assessing whether ground on which an installed infrastructure or environment or planned infrastructure or environment at a location represented by the data element is expected to flood or be inundated based on the water height or elevation; and/or 
 calculating a depth of a flood at a location represented by the data element based on the water height or elevation. 
   
     
     
         29 . A method for reducing vertical bias and/or root mean square error (RMSE) of an elevation dataset, comprising:
 providing input data to a convolution neural network (NN), the NN comprising:
 an input layer; 
 a plurality of hidden layers connected to the input layer, the plurality of hidden layers configured to iteratively analyze the input data and learn nonlinear relationships between the input data and actual elevation; and 
 an output layer connected to the plurality of hidden layers, the output layer configured to output a predicted elevation based on the analysis of the input data; and 
   storing the predicted elevation.